Computer Science > Computer Vision and Pattern Recognition

Abstract: Attentional Neural Network is a new framework that integrates top-down
cognitive bias and bottom-up feature extraction in one coherent architecture.
The top-down influence is especially effective when dealing with high noise or
difficult segmentation problems. Our system is modular and extensible. It is
also easy to train and cheap to run, and yet can accommodate complex behaviors.
We obtain classification accuracy better than or competitive with state of art
results on the MNIST variation dataset, and successfully disentangle overlaid
digits with high success rates. We view such a general purpose framework as an
essential foundation for a larger system emulating the cognitive abilities of
the whole brain.